Teachers Use of Data, Measurement, and Data Modeling in Quantitative Reasoning (Petrosino, 2016)

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This chapter was inspired and motivated by participation in the Quantitative Reasoning session of the Waterbury Summit on “Reforming STEM Education – The Central Role ofPractices” held in University Park, PA in Summer 2013. The broad goals of the Summit were to discuss the redesign of K-12 education for alignment of curriculum-instruction-assessment; STEM teacher education; post-secondary STEM education and informal education on the publics’ engagement. Presentations and discussions centered on opportunities and challenges the Next Generation Science Standards present for (1) initial preparation of and continuing professional development of STEM educators and (2) the general education of the publics’ understandings about science and engineering practices. Overall, the summit stimulated a reconceptualization of STEM education with a focus on systems thinking, model-based reasoning, and quantitative reasoning. 

To organize this chapter, I articulate the role of data modeling including an emphasis on variability as a means of pursuing meaningful investigative activities in STEM classrooms. I continue by addressing some key components of an instructional sequence for scaffolding measurement, variability, and modeling in the pursuit of experiment in K-12 settings. Next, I discuss the role of pre-service teacher education and professional development in attempting to take some of these reform pedagogies to a larger scale than simple single classroom implementations. I conclude by discussing how quantification and measurement could forge links between integrating science and mathematics education.

Petrosino, A. J. (2016). Teachers Use of Data, Measurement, and Data Modeling in Quantitative Reasoning. In R. Duschl and A. Bismarck (Eds), Reconceptualizing STEM Education: The Central Role ofPractices (pp.167-180). New York: Taylor & Francis/Routledge.

  Petrosino (2016) Data Modeling